The present application hereby claims priority under 35 U.S.C. xc2xa7119 on German patent application number DE 10229113.6 filed Jun. 28, 2002, the entire contents of which are hereby incorporated herein by reference.
With modem medical diagnostic methods, such as X-ray computed tomography (CT), image data can be obtained from a measured object that has been examined. As a rule, the measured object that has been examined is a patient.
X-ray computed tomographyxe2x80x94designated CT for short belowxe2x80x94is a specific X-ray recording method which, in terms of image structure, differs fundamentally from the classical X-ray layer recording method. In the case of CT recordings, transverse slices are obtained, that is to say depictions of body layers which are oriented substantially at right angles to the axis of the body. The tissue-specific physical variable represented in the image is the distribution of the attenuation of X radiation xcexc(x,y) in the section plane. The CT image is obtained by way of reconstruction of the one-dimensional projections, supplied by the measuring system used, of the two-dimensional distribution of xcexc(x,y) from numerous different viewing angles.
The projection data is determined from the intensity I of an X-ray after its path through the layer to be depicted and its original intensity I0 at the X-ray source in accordance with the absorption law:                               ln          ⁢                      I                          I              0                                      =                              ∫            L                    ⁢                                    μ              ⁡                              (                                  x                  ,                  y                                )                                      ⁢                          ⅆ              l                                                          (        1        )            
The integration path L represents the path of the X-ray considered through the two-dimensional attenuation distribution xcexc(x,y). An image projection is then composed of the measured values of the linear integrals through the object layer obtained with the X-rays from one viewing direction.
The projections originating from an extremely wide range of directionsxe2x80x94characterized by the projection angle xcex1xe2x80x94are obtained by way of a combined X-ray detector system, which rotates about the object in the layer plane. The devices which are most common at present are what are known as xe2x80x9cfan ray devicesxe2x80x9d, in which tubes and an array of detectors (a linear arrangement of detectors) in the layer plane rotates jointly about a centre of rotation which is also the centre of the circular measurement field. The xe2x80x9cparallel beam devicesxe2x80x9d, afflicted by very long measuring times, will not be explained here. However, it should be pointed out that transformation from fan to parallel projections and vice versa is possible, so that the present invention, which is to be explained by using a fan beam device, can also be applied without restriction to parallel beam devices.
In the case of fan beam geometry, a CT recording includes linear integral measured values xe2x88x921n(I/I0) of incoming beams, which are characterized by a two-dimensional combination of the projection angle xcex1xcex5[0,2xcfx80] and the fan angles xcex2xcex5[xe2x88x92xcex20,xcex20](xcex20 is half the fan opening angle) which define the detector positions. Since the measuring system only has a finite number k of detector elements, and a measurement consists of a finite number y of projections, this combination is discrete and can be represented by a matrix:
{tilde over (p)}(xcex1y,xcex2k):[0, 2xcfx80)xc3x97[xe2x88x92xcex20, xcex20]xe2x80x83xe2x80x83(2)
or
{tilde over (p)}(y,k):(1, 2, . . . NP)xc3x97(1, 2, . . . NS)xe2x80x83xe2x80x83(3)
The matrix {tilde over (p)}(y, k) is called the sinugram for fan beam geometry. The projection number y and the channel number k are of the order of magnitude of 1000.
If the logarithms are formed in accordance with equation (1), then the linear integrals of all the projections                               p          ⁡                      (                          α              ;              β                        )                          =                              ln            ⁢                          I                              I                0                                              =                      -                                          ∫                L                            ⁢                                                μ                  ⁡                                      (                                          x                      ,                      y                                        )                                                  ⁢                                  ⅆ                  l                                                                                        (        2        )            
are therefore obtained, their entirety also being referred to as the radon transform of the distribution xcexc(x,y). Such a radon transformation is reversible, and accordingly xcexc(x,y) can be calculated from p(xcex1,xcex2) by back-transformation (inverse radon transformation).
In the back-transformation, a convolution algorithm is normally used, in which the linear integrals for each projection are firstly convoluted with a specific function and then back-projected onto the image plane along the original beam directions. This specific function, by which the convolution algorithm is substantially characterized, is referred to as a xe2x80x9cconvolution corexe2x80x9d.
By way of the mathematical configuration of the convolution core, there is the possibility of influencing the image quality specifically during the reconstruction of a CT image from the raw CT data. For example, by way of an appropriate convolution core, high frequencies can be emphasized, in order to increase the local resolution in the image, or by way of a convolution core of an appropriately different nature, high frequencies can be damped in order to reduce the image noise. In summary, therefore, it is possible to state that, during the image reconstruction in computed tomography, by selecting a suitable convolution core, the image characteristic, which is characterized by image sharpness/image contrast and image noise (the two behave in a fashion complementary to each other), can be influenced.
The principle of image reconstruction in CT by calculating the xcexc-value distribution will not be discussed further. An extensive description of CT image reconstruction is presented, for example, in xe2x80x9cBildgebende Systeme fxc3xcr die medizinische Diagnostikxe2x80x9d [Imaging systems for medical diagnostics], 3rd ed, Munich, Publicis MCD Verlag, 1995, author: Morneburg Heinz, ISBN 3-89578-002-2.
However, the task of image reconstruction has not yet been completed with the calculation of the xcexc-value distribution of the transilluminated layer. The distribution of the attenuation coefficient xcexc in the medical area of application merely represents an anatomical structure, which still has to be represented in the form of an X-ray image.
Following a proposal by G. N. Hounsfield, it has become generally usual to transform the values of the linear attenuation coefficient xcexc (which has the dimensional unit cmxe2x88x921) to a dimensionless scale, in which water is given the value 0 and air the value xe2x88x921000. The calculation formula for this xe2x80x9cCT indexxe2x80x9d is:                               CT          ⁢                      xe2x80x83                    ⁢          index                =                                            μ              -                              μ                water                                                    μ              water                                ⁢          1000                                    (        4        )            
The unit of the CT index is called the xe2x80x9cHounsfield unitxe2x80x9d (HU). This scale, referred to as the xe2x80x9cHounsfield scalexe2x80x9d, is very well suited to the representation of anatomical tissue, since the unit HU expresses the deviation in parts per thousand from xcexcwater and the xcexc values of most substances inherent in the body differ only slightly from the xcexc value of water. From the numerical range (from xe2x88x921000 for air to about 3000), only whole numbers are used to carry the image information.
However, the representation of the entire scale range of about 4000 values would by far exceed the discriminating power of the human eye. In addition, it is often only a small extract from the attenuation value range which is of interest to the observer, for example the differentiation between gray and white brain substance, which differ only by about 10 HU.
For this reason, use is made of what is known as image windowing. In this case, only part of the CT value scale is selected and spread over all the available gray stages. In this way, even small attenuation differences within the selected window become perceptible gray tone differences, while all CT values below the window are represented as black and all CT values above the window are represented as white. The image window can therefore be varied as desired in terms of its central level and also in terms of its width.
Now, in computed tomography, it is of interest in specific recordings to perform organ-specific settings of the image characteristic and, under certain circumstances, organ-specific windowing. For example, in the case of transverse slices through the breast cavityxe2x80x94in which heart, lungs, spinal column are recorded at the same timexe2x80x94organ-specific optimization of the image representation leads to a far better overview and makes it easier for the user to interpret the CT recording.
A recording optimized in this way is made, in accordance with the prior art, in that, following recording of the relevant layer, by using different convolution cores during the image reconstruction from the raw data, a series of images is produced which in each case differ from one another in terms of different image characteristics (contrast, noise). The user then decides in which image the respective organ is represented optimally in accordance with the diagnostic requirement. In the selected images, the user must segmentxe2x80x94in other words: xe2x80x9cmarkxe2x80x9d and xe2x80x9ccut outxe2x80x9dxe2x80x94the respective organ and insert it into the final image. For the purpose of segmentation, what are known as segmentation algorithms are available to the user. These generally function in such a way that, within the organ to be segmented, a starting point is set by the user, from which the edge of the organ is determined in accordance with different points of view. The algorithm is moved along the organ boundary until the entire organ has been scanned and therefore cut out and can be inserted into the final image.
The procedure during segmentation of this type according to the prior art is very time-consuming, since the user has to analyze the entire series of images. Secondly, when cutting out and inserting the segmented organ, no image information must be lost in the transition region (marginal region of the organ), which is not guaranteed in the case of current segmentation algorithmsxe2x80x94which additionally (as far as development and computing power are concerned) are extremely complicated.
Distinguishing organs in a representation can also be carried out by way of a transfer function, which finds and delimits anatomically associated gray value regions in the CT image. This is possible since the attenuation factors in the Hounsfield scale, HU values, as they are known, occupy different regions, depending on the organ. Typically, a transfer function allocates all organ-specific attenuation factors a specific gray value or a specific color. In DE 100 52 540 A1 a diagnostic device is described in which interactive determination of organ-specific gray value regions in a medical image is made possible.
For this purpose, a histogram is created from the raw data of the medical image and visualized on a user interface. By way of a dialogue interface, the user can then enter values for the determination of an organ-specific gray value region and obtains these values represented as a trapezoidal function in the histogram itself. The values entered thus determine the range of the gray values to be treated and their colored representation in the image, such as color, brightness and hiding power. Values at the edges of the set gray value region are converted with a higher transparency than those in the central region of the selected gray value interval.
The disadvantage in this case is that adjacent, similar cases exhibit attenuation or gray values in a coherent region of the histogram. The use of the transfer function thus leads to indistinguishability of the two tissues in the image treated.
It is therefore an object of an embodiment of the present invention to propose a technique for improving organ-specific image optimization which, in particular, effects optical separation of adjacent tissues of similar consistency in the resulting image.
An object may achieved in particular by a method according to an embodiment of the invention for organ-specific image optimization in computed tomography, the HU values of a layer of the body previously recorded by a CT device being calculated and, on this basis, a first CT image being created. For this first CT image, a histogram is also created, in which the frequency distribution of the HU values is reproduced. In the histogram, at least one organ-specific HU region is defined and this is allocated an HU-dependent transfer function. Furthermore, a second CT image is created on the basis of the previously calculated HU values for the recorded layer of the body. The first and second CT image are filtered with the HU-dependent transfer function and, finally, the filtered first CT image is mixed with the filtered second CT image.
Furthermore, an object may be achieved by a computed tomography device which includes a computer for processing measured data and a monitor for visualizing the data processed by the computer, the computer being designed to carry out a method according to an embodiment of the invention.
Furthermore, an object may be achieved by a computer program product which has a series of physical states which are suitable to be implemented by a computing device which is connected to a computed tomography device in such a way that a method according to an embodiment of the invention is carried out on a computed tomography device.
Mixing two images indicates that both the characteristics of the first and the characteristics of the second image are expressed in the resulting mixed image, with which, for example, tissue boundaries together with tissue types can be represented.
An improvement in the result can be achieved if a first image filter is used for creating the first CT image and/or a second image filter is used for creating the second CT image. As an alternative to this, the first CT image can also be created with the aid of a first convolution core, the second CT image also or additionally with a second convolution core. In both methods, the first CT image is advantageously reproduced as a smoothed CT image, the second CT image, if required, as a high-contrast CT image. In this case, the terms first and second CT image relate only to the distinguishability of the two images, not to a certain order, so that the first CT image can also have high contrast, while the second has a smoothed characteristic.
With regard to good representation of an organ-specific tissue in the resulting image, the magnitude of the HU-dependent transfer function moves in an interval between 0 and 1, it being further possible for the mixing of the first CT image with the second CT image to be carried out expediently as a weighted, pixel by pixel addition of the first CT image to the second CT image. In a preferred embodiment of the present invention, the weighting factor of the first CT image in this case corresponds to the magnitude of the HU-dependent transfer function, and the weighting factor of the second CT image corresponds to the difference between the magnitude of the HU-dependent transfer function and the value 1. In an alternative, likewise preferred embodiment, the weighting factor of the second CT image corresponds to the magnitude of the HU-dependent transfer function, and the weighting factor of the first CT image corresponds to the difference between the magnitude of the HU-dependent transfer function and the value 1.
Furthermore, in order to create the first and/or second CT image, a two-dimensional, separable image filter can be used. As alternative to this, however, use can also be made of a one-dimensional image filter to create the first and/or the second CT image.
Simple detection of organ-specific tissue regions in the resulting CT image can be achieved by different, organ-specific HU regions being represented with different windowing.